Travel-time models based on speed predictions are a valuable tool for provide traffic information to motorists, and for traffic monitoring and planning by those responsible for transportation network infrastructure management. Many modeling approaches are currently in use that model predictions of travel times based on traffic speed. These existing approaches include regression methods, (such as linear regression and neural networks), nearest-neighbor methods, and other machine learning techniques (such as Random Forests, support vector machines, etc.). Other existing approaches use time-series modeling, like ARIMA or Kalman filtering. Other, more complex models utilize micro-simulation techniques that try to build a representation of the physical street and traffic system.
Regardless, the inclusion of weather information in these existing modeling approaches is uncommon. Where weather information is included, it is based on real-time or historical data only, and does not take into account the impact that a future state of precipitation may have on traffic speed. Therefore there is no known methodology for augmenting travel time predictions with precipitation over some future time interval.